The Psychology of Billing
Bibliographic record
Abstract
Abstract Contracting between tax entities and tax professionals occurs millions of times every year, yet little is known about the nature of these economic interactions. This study examines the effect of commonly occurring contextual factors on tax professionals’ billing decisions for tax research. These contextual factors are unrelated to the tax research itself and the time it takes to conduct the tax research, but we find that billing decisions are strongly influenced by the three non‐time‐related contextual factors that we manipulate. Initial client volume impacts amounts billed for tax research, with lower initial client volume resulting in higher per client fees. Further, we find that initial billing decisions serve as value billing benchmarks for unanticipated subsequent clients who benefit from research conducted for initial clients. As a result, subsequent clients are billed higher fees when they follow a smaller number of initial clients. We also find that client referrals are billed higher fees than nonclient referrals because professionals attempt to avoid making initial clients feel as though they have been treated unfairly relative to subsequent clients who would otherwise be billed lower fees. The results of this study are relevant beyond the traditional confines of accounting research—they are relevant to the millions of tax entities that contract with tax professionals each year.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".